Accepted author manuscript, 925 KB, PDF document
Available under license: CC BY-NC-SA: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Accepted author manuscript
Licence: CC BY-NC-SA: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Research output: Working paper › Preprint
Research output: Working paper › Preprint
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TY - UNPB
T1 - Exact statistical analysis for response-adaptive clinical trials
T2 - a general and computationally tractable approach
AU - Baas, Stef
AU - Jacko, Peter
AU - Villar, Sofía S.
N1 - 35 pages, 6 figures, 11 tables
PY - 2024/7/1
Y1 - 2024/7/1
N2 - Response-adaptive (RA) designs of clinical trials allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. RA designs face greater regulatory scrutiny due to potential type I error inflation, which limits their uptake in practice. Existing approaches to type I error control either only work for specific designs, have a risk of Monte Carlo/approximation error, are conservative, or computationally intractable. We develop a general and computationally tractable approach for exact analysis in two-arm RA designs with binary outcomes. We use the approach to construct exact tests applicable to designs that use either randomized or deterministic RA procedures, allowing for complexities such as delayed outcomes, early stopping or allocation of participants in blocks. Our efficient forward recursion implementation allows for testing of two-arm trials with 1,000 participants on a standard computer. Through an illustrative computational study of trials using randomized dynamic programming we show that, contrary to what is known for equal allocation, a conditional exact test has, almost uniformly, higher power than the unconditional test. Two real-world trials with the above-mentioned complexities are re-analyzed to demonstrate the value of our approach in controlling type I error and/or improving the statistical power.
AB - Response-adaptive (RA) designs of clinical trials allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. RA designs face greater regulatory scrutiny due to potential type I error inflation, which limits their uptake in practice. Existing approaches to type I error control either only work for specific designs, have a risk of Monte Carlo/approximation error, are conservative, or computationally intractable. We develop a general and computationally tractable approach for exact analysis in two-arm RA designs with binary outcomes. We use the approach to construct exact tests applicable to designs that use either randomized or deterministic RA procedures, allowing for complexities such as delayed outcomes, early stopping or allocation of participants in blocks. Our efficient forward recursion implementation allows for testing of two-arm trials with 1,000 participants on a standard computer. Through an illustrative computational study of trials using randomized dynamic programming we show that, contrary to what is known for equal allocation, a conditional exact test has, almost uniformly, higher power than the unconditional test. Two real-world trials with the above-mentioned complexities are re-analyzed to demonstrate the value of our approach in controlling type I error and/or improving the statistical power.
KW - stat.ME
M3 - Preprint
BT - Exact statistical analysis for response-adaptive clinical trials
PB - Arxiv
ER -